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<p>Monetary aggregates provide valuable information about the business cycle and monetary policy transmission. This paper applies machine learning methods, namely learning vector quantisation (LVQ) and its distinction-sensitive extension (DSLVQ), to identify turning points in euro area M1 and M3. We benchmark performance against the Bry–Boschan algorithm and standard classifiers. Our results show that LVQ detects M1 turning points with only a three-month delay, halving the six-month confirmation lag of Bry–Boschan dating. DSLVQ delivers comparable accuracy while offering interpretability: it assigns weights to the sources of broad money growth, showing that lending to households and firms, as well as Eurosystem asset purchases when present, are the main drivers of turning points in M3. The findings are robust across parameter choices, bootstrap designs, alternative performance metrics, and comparison models. These results demonstrate that machine learning can yield more timely and interpretable signals from monetary aggregates. For policymakers, this approach enhances the information set available for assessing near-term economic dynamics and understanding the transmission of monetary policy.</p>

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A machine learning approach to real-time identification of turning points in monetary aggregates M1 and M3

  • 期刊论文

  • 发表日期：

    2026年4月18日

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  • Max Lampe,
  • Ramón Adalid

摘要

Monetary aggregates provide valuable information about the business cycle and monetary policy transmission. This paper applies machine learning methods, namely learning vector quantisation (LVQ) and its distinction-sensitive extension (DSLVQ), to identify turning points in euro area M1 and M3. We benchmark performance against the Bry–Boschan algorithm and standard classifiers. Our results show that LVQ detects M1 turning points with only a three-month delay, halving the six-month confirmation lag of Bry–Boschan dating. DSLVQ delivers comparable accuracy while offering interpretability: it assigns weights to the sources of broad money growth, showing that lending to households and firms, as well as Eurosystem asset purchases when present, are the main drivers of turning points in M3. The findings are robust across parameter choices, bootstrap designs, alternative performance metrics, and comparison models. These results demonstrate that machine learning can yield more timely and interpretable signals from monetary aggregates. For policymakers, this approach enhances the information set available for assessing near-term economic dynamics and understanding the transmission of monetary policy.